How OpenAI Codex Manages Context Limits: A Hybrid Control Design Explained

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A technical analysis of OpenAI Codex's context compaction system examines how the software handles the challenge of maintaining long-running AI agent sessions within finite context windows. The system uses a hybrid approach where the runtime enforces hard capacity limits at 90% and 95% context usage, while the model is given a limited opportunity to choose a better moment to transition. The 90% threshold triggers a planned rollover, while the 95% threshold acts as a last-resort safety guard, keeping the two concerns deliberately separate. This division of responsibility ensures that the component capable of enforcing resource limits retains final authority, rather than relying solely on the model's judgment. The analysis is based on a specific Codex commit and focuses on the architectural reasoning behind these design choices rather than a line-by-line code review.

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